Update Sales tab to include delivery and party pack

streamlit_app.py

@@ -86,6 +86,11 @@ LANG = {
         "sm_chart_cap":        "%Cap — Capacity utilised",
         "sm_chart_premium":    "%Premium — premium share of customers",
         "sm_chart_rounds":     "Customers by Round (monthly)",
+        "sm_col_normal":       "Normal",
+        "sm_col_premium":      "Premium",
+        "sm_col_delivery":     "Delivery",
+        "sm_col_partypack":    "Party Pack",
+        "sm_chart_rev_split":  "Monthly Revenue by Channel",
         # Forecast tab
         "fc_month_title":     "This Month Forecast",
         "fc_month_customers": "Forecast Customers (this month)",
@@ -207,6 +212,11 @@ LANG = {
         "sm_chart_cap":        "%ใช้พื้นที่",
         "sm_chart_premium":    "%ลูกค้าพรีเมียม",
         "sm_chart_rounds":     "ลูกค้าตามรอบ (รายเดือน)",
+        "sm_col_normal":       "ปกติ",
+        "sm_col_premium":      "พรีเมียม",
+        "sm_col_delivery":     "เดลิเวอรี่",
+        "sm_col_partypack":    "พาร์ตี้แพ็ค",
+        "sm_chart_rev_split":  "รายได้รายเดือนตามช่องทาง",
         # Forecast tab
         "fc_month_title":     "พยากรณ์ของเดือนนี้",
         "fc_month_customers": "พยากรณ์จำนวนลูกค้า (เดือนนี้)",
@@ -1479,6 +1489,97 @@ with tab_summary:
         m = monthly.copy().sort_values(
             ["Year", "Month", "Branch"], ascending=[True, True, True]
         ) if not monthly.empty else monthly.copy()
+
+        # ── Split Revenue into Normal / Premium / Delivery / Party Pack ──
+        # Per-row revenue = GrossRev + SVC (net of discount, including
+        # service charge but excluding tax — matches how the ops team
+        # accounts for revenue).
+        #
+        # Tagging differs between the two restaurants:
+        #   • Copper Buffet uses Type='Package' + SubType ∈
+        #     ('Normal', 'Premium', 'Delivery', 'Party Pack').
+        #   • Tiew Copper is à la carte, so its delivery is tagged with
+        #     Type='Delivery' (no SubType split). It has no Premium /
+        #     Party Pack channels — those columns stay at 0.
+        channel_cols: list[str] = []
+        if not m.empty and not fact_items.empty:
+            fi = fact_items.copy()
+            if "Date" in fi.columns:
+                fi["Date"] = pd.to_datetime(fi["Date"], errors="coerce")
+                fi = fi.dropna(subset=["Date"])
+            if "Restaurant" in fi.columns:
+                fi = fi[fi["Restaurant"] == restaurant_name]
+            if sel_branches and "Branch" in fi.columns:
+                fi = fi[fi["Branch"].isin(sel_branches)]
+            # Apply the same date-range filter the rest of the Summary tab uses.
+            if date_from is not None and "Date" in fi.columns:
+                fi = fi[fi["Date"] >= pd.to_datetime(date_from)]
+            if date_to is not None and "Date" in fi.columns:
+                fi = fi[fi["Date"] <= pd.to_datetime(date_to)]
+            if not fi.empty and "Year" not in fi.columns:
+                fi["Year"]  = fi["Date"].dt.year
+                fi["Month"] = fi["Date"].dt.month
+
+            # Revenue per row = GrossRev + SVC. Both coerced to numeric
+            # (NaN→0) so the sum is safe when columns are missing.
+            if not fi.empty:
+                _gross = pd.to_numeric(fi.get("GrossRev", 0), errors="coerce").fillna(0)
+                _svc   = pd.to_numeric(fi.get("SVC",      0), errors="coerce").fillna(0)
+                fi["_rev"] = _gross + _svc
+
+            def _bucket_into(source: pd.DataFrame, dest_col: str) -> None:
+                """Sum `source['_rev']` per (Year, Month, Branch) into m[dest_col]."""
+                nonlocal m
+                if source.empty:
+                    m[dest_col] = 0.0
+                    return
+                agg = (
+                    source.groupby(["Year", "Month", "Branch"], as_index=False)["_rev"]
+                          .sum()
+                          .rename(columns={"_rev": dest_col})
+                )
+                m = m.merge(agg, on=["Year", "Month", "Branch"], how="left")
+                m[dest_col] = m[dest_col].fillna(0.0)
+
+            if restaurant_name == "Copper Buffet":
+                # Filter to Package rows, then bucket by SubType.
+                fi_pkg = (fi[fi["Type"] == "Package"]
+                          if "Type" in fi.columns else fi.iloc[0:0])
+                def _by_subtype(sub_value: str) -> pd.DataFrame:
+                    if "SubType" not in fi_pkg.columns:
+                        return fi_pkg.iloc[0:0]
+                    return fi_pkg[fi_pkg["SubType"] == sub_value]
+                _bucket_into(_by_subtype("Normal"),     "Normal")
+                _bucket_into(_by_subtype("Premium"),    "Premium")
+                _bucket_into(_by_subtype("Delivery"),   "Delivery")
+                _bucket_into(_by_subtype("Party Pack"), "PartyPack")
+
+            elif restaurant_name == "Tiew Copper":
+                # Tiew Copper tags delivery via Type='Delivery'; the
+                # rest of the revenue is "Normal" (à la carte food +
+                # beverage). Derive Normal as Revenue − Delivery so the
+                # column lines up with the kpi_monthly Revenue total
+                # that drives the other tiles.
+                delivery_rows = (fi[fi["Type"] == "Delivery"]
+                                 if "Type" in fi.columns else fi.iloc[0:0])
+                _bucket_into(delivery_rows, "Delivery")
+                if "Revenue" in m.columns:
+                    m["Normal"] = (m["Revenue"] - m.get("Delivery", 0.0)).clip(lower=0)
+                else:
+                    m["Normal"] = 0.0
+                # Tiew Copper has no Premium / Party Pack channels.
+                m["Premium"]   = 0.0
+                m["PartyPack"] = 0.0
+
+            else:
+                # Group-level rows (Holding / CK / Conso) — no channel
+                # split applies. Leave the columns at 0 for consistency.
+                for c in ("Normal", "Premium", "Delivery", "PartyPack"):
+                    m[c] = 0.0
+
+            channel_cols = [c for c in ("Normal", "Premium", "Delivery", "PartyPack")
+                            if c in m.columns]
+
         round_cols: list[str] = []
         if (restaurant_name == "Copper Buffet"
                 and not fact_shift_items.empty and not m.empty):
@@ -1676,6 +1777,64 @@ with tab_summary:
                                   xaxis_title=None, yaxis_title=None)
                 st.plotly_chart(style_plotly(fig, height=340), use_container_width=True)
 
+            # Stacked-bar revenue split — Normal / Premium / Delivery /
+            # Party Pack per month, summed across the branches in scope.
+            # Skipped only when every channel column is flat-zero in the
+            # filter window.
+            if channel_cols and any(
+                (col in m.columns) and m[col].sum() > 0
+                for col in channel_cols
+            ):
+                _RC_COLOR = {
+                    t("sm_col_normal"):    "#976A4D",  # COPPER (primary baseline)
+                    t("sm_col_premium"):   "#1E2B3A",  # NAVY (premium = anchor)
+                    t("sm_col_delivery"):  "#DC7D3D",  # TIEW (delivery accent)
+                    t("sm_col_partypack"): "#D4A574",  # GOLD (party pack accent)
+                }
+                rev_long = mf[["YearMonth"] + channel_cols].copy()
+                # Rename the internal column names to their localized
+                # labels before melting so the chart legend reads in the
+                # user's language.
+                rev_long = rev_long.rename(columns={
+                    "Normal":    t("sm_col_normal"),
+                    "Premium":   t("sm_col_premium"),
+                    "Delivery":  t("sm_col_delivery"),
+                    "PartyPack": t("sm_col_partypack"),
+                })
+                value_vars = [
+                    t("sm_col_normal"),
+                    t("sm_col_premium"),
+                    t("sm_col_delivery"),
+                    t("sm_col_partypack"),
+                ]
+                value_vars = [c for c in value_vars if c in rev_long.columns]
+                long_df = rev_long.melt(
+                    id_vars=["YearMonth"], value_vars=value_vars,
+                    var_name="Channel", value_name="Revenue",
+                )
+                long_df = (
+                    long_df.groupby(["YearMonth", "Channel"], as_index=False)["Revenue"]
+                           .sum()
+                           .sort_values(["YearMonth"])
+                )
+                fig = px.bar(
+                    long_df, x="YearMonth", y="Revenue", color="Channel",
+                    barmode="stack",
+                    color_discrete_map=_RC_COLOR,
+                    category_orders={"Channel": value_vars},
+                    text="Revenue",
+                )
+                fig.update_traces(
+                    texttemplate="฿%{y:,.0f}", textposition="inside",
+                    textfont=dict(size=9, color="#FAF7F2"),
+                    insidetextanchor="middle",
+                )
+                fig.update_yaxes(tickformat=",.0f")
+                fig.update_layout(title=t("sm_chart_rev_split"),
+                                  xaxis_title=None, yaxis_title=None,
+                                  legend_title=None)
+                st.plotly_chart(style_plotly(fig, height=340), use_container_width=True)
+
         # ── Monthly summary table ────────────────────────────────────────
         st.markdown(f"**{t('sm_monthly_summary')}**")
         if not m.empty:
@@ -1683,9 +1842,9 @@ with tab_summary:
                          ["Year", "Month", "Branch", "Revenue", "Customers"]
                          if c in m.columns]
             tail_cols = [c for c in ["Rev_Per_Head"] if c in m.columns]
-            # Column order: base · %Cap · %Premium · Rev/Head · rounds.
+            # Column order: base · channels · %Cap · %Premium · Rev/Head · rounds.
             metric_cols = [c for c in ["%Cap", "%Premium"] if c in m.columns]
-            cols = base_cols + metric_cols + tail_cols + round_cols
+            cols = base_cols + channel_cols + metric_cols + tail_cols + round_cols
 
             # Pre-format money / count / percent columns to strings (printf
             # "," flag is not supported on older Streamlit versions).
@@ -1695,11 +1854,19 @@ with tab_summary:
                     disp[c] = disp[c].map(fmt_money)
             if "Customers" in disp.columns:
                 disp["Customers"] = disp["Customers"].map(fmt_num)
+            for c in channel_cols:
+                disp[c] = disp[c].map(fmt_money)
             for c in round_cols:
                 disp[c] = disp[c].map(fmt_num)
             for c in metric_cols:
                 disp[c] = disp[c].map(fmt_pct)
-            disp = disp.rename(columns={"Rev_Per_Head": "Rev / Head"})
+            disp = disp.rename(columns={
+                "Rev_Per_Head": "Rev / Head",
+                "Normal":       t("sm_col_normal"),
+                "Premium":      t("sm_col_premium"),
+                "Delivery":     t("sm_col_delivery"),
+                "PartyPack":    t("sm_col_partypack"),
+            })
             st.dataframe(
                 disp,
                 use_container_width=True, hide_index=True,